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Neural solutions to interact with computers by hand gesture recognition

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Abstract

This paper attempts to present a vision-based interface which interacts with computers by hand gesture recognition. This work aims at creating a natural and intuitive application employing both static and dynamic hand gestures. The proposed application can be summarized in three main steps: hands detection in a video, hands tracking and converting hand shapes or trajectories into computer commands. To accomplish this application, a classification phase is paramount whether at the part of hand detection, or at the phase of “commanding computers”. For this reason, we have proposed to use a wavelet network classifier (WNC) learnt by fast wavelet transform (FWT). To emphasize the robustness of this classifier, we have used a neural network classifier (NNC) version in order to compare the two classifiers’ performances aiming at proving the strength of our proposed one. Global rates given by experimental results show the effectiveness of our proposed approaches of hand detection, hand tracking and hand gesture recognition. The comparison of the two classifier’s result helps to choose the best classifier, which can improve the performances of our application.

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Acknowledgements

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Authors and Affiliations

  1. REGIM: Research Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia

    Tahani Bouchrika, Mourad Zaied, Olfa Jemai & Chokri Ben Amar

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  1. Tahani Bouchrika
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  2. Mourad Zaied
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  3. Olfa Jemai
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  4. Chokri Ben Amar
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Correspondence to Tahani Bouchrika.

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Bouchrika, T., Zaied, M., Jemai, O. et al. Neural solutions to interact with computers by hand gesture recognition. Multimed Tools Appl 72, 2949–2975 (2014). https://doi.org/10.1007/s11042-013-1557-y

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